Talk given at a Machine Learning meetup in Sao Paulo.

1. Explainable Machine
Learning
Gabriel Cypriano

2. Do we need to
explanations?

3. Linear models to the rescue

4. But...
What if we could train a Random
Forest that seems to perform much
better?

5. Random Forest Feature Importances

6. Decision Paths
Prediction for:
RM LSTAT NOX DIST
3.1 4.5 0.54 2.6
http://blog.datadive.net/interpreting-ra
ndom-forests

7. treeinterpreter — Interpreting predictions with Decision Paths

8. treeinterpreter — Explaining difference between 2 datasets with Decision Paths

9. treeinterpreter — What about feature interactions?

10. treeinterpreter — Classification on the Iris
dataset

11. treeinterpreter / Pivotal — Explanation on steroids — give me some dataviz

12. treeinterpreter / Pivotal — Contribution vs Feature Value (single Decision Tree)

13. treeinterpreter / Pivotal — Contribution vs Feature Value (Random Forest)

14. treeinterpreter / Pivotal — Contribution vs Feature Value (single Decision Tree)

15. treeinterpreter / Pivotal — Contribution vs Feature Value (Random Forest)

16. treeinterpreter / Pivotal — Classification
Feature Contributions Violin Plot (for class “Infant”)

17. treeinterpreter / Pivotal — Classification
Contributions to each class vs Feature Value

18. What about
Boosted Trees?
Instead of averaging contributions
across trees we just have have to
sum them.
Available with:
● ELI5
e.g., XGBoost, LightGBM

19. ELI5 — XGBoost — Feature Importances (Titanic dataset)

20. ELI5 — XGBoost prediction — Titanic dataset

21. Model-agnostic explanations
e.g., for non-tree based models

22. Lime
● Local
approximations
● Model agnostic
● Can select a set of
representative
instances with
explanations

23. Lime — Explanation

24. Lime — creating superpixels to aid in image recognition

25. Lime — image recognition

26. Lime for NLP

27. More use cases
● Learn the right features (dogs
vs wolves)
● Understand whether model
overfits a particular feature
● Identifying data leakage
● Dataset shift (training data
different than test data)
● Pneumonia/asthma case
Amazon, Netflix

28. ● Not only useful for when things aren't working
● Different costs for making mistakes

29. References
Interpreting Random Forests
Random forest interpretation with scikit-learn
Random forest interpretation – conditional feature contributions
Interpreting Decision Trees and Random Forests
XGBoost Decision Paths
Explaining XGBoost predictions on the Titanic dataset
“Why Should I Trust You?” Explaining the Predictions of Any Classifier

30. Tools
treeinterpreter
Lime
ELI5

31. Gracias!
gabrielcs.me
vagas.creditas.com.br
somostera.com